You’re reading The Steady Beat, a weekly pulse of must-reads for anyone orchestrating teams, people, and agents across the modern digital workplace – whether you’re managing sprints, driving roadmaps, leading departments, or just making sure the right work gets done. Curated by the team at Steady.
The Real Moat
Microsoft’s 2026 Work Trend Index has the headline most coverage led with – Microsoft 365 agent use up 15x year-over-year – but the finding that actually matters is buried in the survey. Across 19,854 respondents in 10 markets, culture, manager support, and talent practices explain 67% of AI’s measurable impact on outcomes. Individual skill explains just 32%. So the system matters twice as much as the operator. Microsoft calls what the top firms are building owned intelligence: AI know-how unique to your business, accumulated over time, impossible to copy. Everyone else seems to be conflating impact with token use and seat licences. Agents generate signals daily – what worked, what failed, where outcomes drifted – and that data is key. The idea the top teams are adopting is absorption over adoption: getting last week’s agent output to reshape next week’s work and decisions.
— Steady, 8m, #ai, #strategy, #leadership
Amplifier
Shrivu Shankar’s take on the 10-20% productivity bump most teams report from AI: that’s the free part – what you get by sprinkling AI onto how you already work. The ceiling above that is set by structure, not tools. The pitfalls are everywhere: AI removes the friction that used to force planning, so you skip the planning. Small tasks get slower because the context overhead outweighs the speed-up. And constantly babysitting loops creates bottlenecks. Coding now is maybe 20% of the cycle. Once AI compresses that, the other 80% – approvals, reviews, syncs – becomes the whole constraint, and a function-shaped org can’t absorb it. Shankar’s fix is to swap function ownership for loop ownership, and to stop scoring teams on usage instead of outcomes.
— Shrivu’s Substack, 10m, #ai, #productivity, #engineering
Buyer’s Remorse
The reversals on the AI-driven layoff cycle are starting to land. Gartner projects 50% of companies that cited AI when cutting headcount will rehire those same functions by 2027, and a Careerminds survey found one in three employers spent more on restaffing than they actually saved. Sarah Choudhary’s argument is that the underlying investment isn’t holding up either: IBM finds only 25% of AI projects deliver promised returns and 16% ever scale, and MIT puts the share of companies that fully embraced AI and saw measurable profit at 5%. The other 95% are cutting people to fund infrastructure that hasn’t paid for itself. Klarna is the canonical case – 700 agents announced as replacements in 2024, walked back publicly in 2025 when quality collapsed. The problem isn’t the technology, it’s FOMO. 64% of CEOs admit they invest before understanding the value because they’re afraid of falling behind. From a conference stage, that reads as vision; on a balance sheet, it’s an unfunded liability. Choudhary’s rule for leaders who actually want to come out ahead: budget the reversal before the layoff. If the business case still works with rehiring costs priced in, proceed. If it doesn’t, you’re not running a strategy.
— Forbes, 7m, #ai, #leadership, #strategy
Bring Your Tools
Banning AI from a 2026 tech interview is like banning the candidate from opening their IDE – it tests the wrong skill against the wrong job. A JetBrains survey found 90% of developers regularly use at least one AI tool at work, which means you could be evaluating people on workflows they haven’t used in years. Gregor Ojstersek argues the interview should match how the work actually happens, and he’s earned standing to say it: he ground through 200+ LeetCode problems himself before concluding none of it tracked with real engineering responsibilities. The deeper issue is that AI-banned coding tests were a poor signal even before AI, filtering for who studied recently rather than who builds well. Stripping AI out now just degrades the signal further. The replacement isn’t permissive vibe-coding either. Build interview tasks where AI can produce a plausible answer, then judge candidates on what they did with it. Did they catch the bug the model missed? Push back on the wrong abstraction? Ask the right clarifying question before generating anything? Modern engineering is judgment plus AI for speed. The interview should measure both, not one minus the other.
— Engineering Leadership, 6m, #hiring, #engineering, #ai
Slop Cannons
Jake Handy has a name for the engineers and designers who treat AI agents as high-throughput artifact factories: slop cannons. They run multiple agents in parallel, ship sprawling PRs that need quick patches to land, and trust model output over peer review. The behavior is spreading, and it’s costing more than it saves. AI-authored PRs on GitHub jumped 325% in six months, and CodeRabbit found them 1.7x buggier than human-written code. A METR study clocked developers reporting they felt 20% faster while actually running 19% slower – a perception gap that compounds in any org running on velocity narratives. Volume is one problem; quality is the other. Models default to agreement 58% of the time instead of pushing back. AI-generated code fails secure coding benchmarks 45% of the time. Developers score 17% lower on conceptual quizzes about code they wrote with AI – they shipped it, but they don’t actually understand it. Handy’s prescription includes capping parallel agents at two, requiring a spec before any of them run, and enforcing reviews.
— Handy AI, 9m, #ai, #engineering, #quality
Echo of the Week
Echoes are AI agents in Steady that automatically gather and deliver work context to teams on a schedule—answering recurring questions about progress, capacity, and coordination so you stop burning hours assembling the same information manually.
Product Progress Overview is the Echo for anyone who’s tired of translating engineering work into business language by hand. Every Friday morning, it pulls the past week’s merged pull requests, organizes them into coherent themes by application area, and delivers a non-technical summary suited for stakeholders who don’t read diffs. Engineering managers reporting to non-technical leadership get a head start on board updates. DevRel teams get the bones of a release note. Product managers get a customer-friendly recap of what shipped. The promise is simple: no more interrupting engineers to ask “what actually got done this week?” The Echo does the translation work automatically, so the people who need visibility get it without anyone losing flow.
The lightweight teamwork OS
Teams rely on two coordination loops to function: a big-picture loop connecting plans to progress, and a ground-level loop keeping teammates in sync.
Problem is, status quo approaches to running those loops are an incomplete, inconsistent, and inefficient tangle of meetings, emails, chat threads, dashboards, and manual toil.
Steady is the teamwork OS that runs both loops for you. Purpose-built agents continuously distill updates and activity into personalized intelligence that keeps everyone aligned and informed automatically.
The outcome: high-performing teams that deliver better work, 3X faster.
Learn more at runsteady.com.